Techno-Economic Analysis and Optimization of a Microgrid Considering Demand-Side Management

Abstract

The control and managing of power demand and supply become very crucial because of penetration of renewables in the electricity networks and energy demand increase in residential and commercial sectors. In this paper, a new approach is presented to bridge the gap between Demand-Side Management (DSM) and microgrid portfolio, sizing and placement optimization. Although DSM helps energy consumers to take advantage of recent developments in utilization of Distributed Energy Resources (DERs) especially microgrids, a huge need of connecting DSM results to microgrid optimization is being felt. Consequently, a novel model that integrates the DSM techniques and microgrid modules in a two-layer configuration is proposed. In the first layer, DSM is employed to minimize the electricity demand (e.g. heating and cooling loads) based on zone temperature set-point. Using the optimal load profile obtained from the first layer, all investment and operation costs of a microgrid are then optimized in the second layer. The presented model is based on the existing optimization platform developed by RU-LESS (Rutgers University, Laboratory for Energy Smart Systems) team. As a demonstration, the developed model has been used to study the impact of smart HVAC control on microgrid compared to traditional HVAC control. The results show a noticeable reduction in total annual energy consumption and annual cost of microgrid.

A topological overview of microgrids: from maturity to the future

“…Literature studies Learning-based algorithms [106], [114], [118], [124], [135], [136] Heuristic algorithms [105], [108], [109], [114], [128], [130], [131], [132], [133], [144], [145] Control algorithm based methods [101], [104], [108], [112], [113], [133], [138], [141], [150] Mixed integer-based algorithms [111], [114], [115], [116], [129], [139], [142], [143] Classification-based methods [37] Game theory-based models [107] Stochastic programming [103], [116], [126], [137] Two-stage robust optimization [121] Dynamic programming [113], [117], [123], [140] Others [100], …”

“…The effects of DSM techniques on MG portfolio, sizing and placement were criticized in [129]. In this study, it was aimed to minimize the cooling load electricity price and optimize all operating and investment costs of MG by using DSM model.…”

The Impact of Occupancy-Driven Models on Cooling Systems in Commercial Buildings

“…In this equation, CC t i and ϕ t i are adjusted to the real operating conditions. These conditions are described in Equations (11) and (12) based on the performance curves that are explained in [36] while using cooling capacity modifier factors and energy input ratio factors.…”

“…In Equation (10), the required cooling energy is calculated based on the total cooling capacity (CC t i ) and energy input ratio (ϕ t i ). The modifier factors CCT t i and EIT t i in Equations (11) and (12) are used to reflect the effect of weather conditions (e.g., outdoor temperature, and humidity) on the nominal cooling capacity (RQ i ) and the coefficient of performance (COP i ). These factors are calculated based on the formulation that is presented in [35].…”

“…Different control methods for HVAC systems are being used to output the optimal setpoint temperatures. These methods include white-box models (utilizing thermodynamic equations for thermal analysis of HVAC systems and building zones) [9][10][11][12][13], statistical and data-driven methods [14][15][16][17], model predictive control algorithms [18][19][20][21], and artificial intelligence models [18,[22][23][24]. Ghofrani and Jafari presented a physical-statistical approach to optimally control a commercial building air-conditioning operation that is based on building thermal physics and human behavior [25].…”